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» Active Mining of Data Streams
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KDD
2006
ACM
129views Data Mining» more  KDD 2006»
14 years 10 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
VLDB
2005
ACM
140views Database» more  VLDB 2005»
14 years 3 months ago
Loadstar: Load Shedding in Data Stream Mining
In this demo, we show that intelligent load shedding is essential in achieving optimum results in mining data streams under various resource constraints. The Loadstar system intro...
Yun Chi, Haixun Wang, Philip S. Yu
PAKDD
2004
ACM
137views Data Mining» more  PAKDD 2004»
14 years 3 months ago
Fast and Light Boosting for Adaptive Mining of Data Streams
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
Fang Chu, Carlo Zaniolo
IPPS
2006
IEEE
14 years 4 months ago
Supporting self-adaptation in streaming data mining applications
There are many application classes where the users are flexible with respect to the output quality. At the same time, there are other constraints, such as the need for real-time ...
Liang Chen, Gagan Agrawal
ICTAI
2007
IEEE
14 years 4 months ago
An Adaptive Distributed Ensemble Approach to Mine Concept-Drifting Data Streams
An adaptive boosting ensemble algorithm for classifying homogeneous distributed data streams is presented. The method builds an ensemble of classifiers by using Genetic Programmi...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...